me {mclust1998} | R Documentation |
EM iteration (M-step followed by E-step) for estimating parameters in an MVN mixture model with possibly one Poisson noise term.
me(data, modelid, z, ...)
data |
matrix of observations. |
modelid |
An integer specifying a parameterization of the MVN covariance matrix
defined by volume, shape and orientation charactertistics of the
underlying clusters. The allowed values for modelid and their
interpretation are as follows: "EI" : uniform spherical,
"VI" : spherical, "EEE" : uniform variance, "VVV" :
unconstrained variance, "EEV" : uniform shape and volume,
"VEV" : uniform shape.
|
z |
matrix of conditional probabilities. z should have a row for
each observation in data , and a column for each component of
the mixture.
|
... |
additional arguments, as follows:
eps varies the parameterization, each
of which has a default.
tol . Default :
sqrt(.Machine$double.eps) .
Inf (no
upper limit).
FALSE .
FALSE .
noise = TRUE ). Default : determined by function hypvol
|
the conditional probablilities at the final iteration (information about the iteration is included as attributes).
The reciprocal condition estimate returned as an attribute ranges in value between 0 and 1. The closer this estimate is to zero, the more likely it is that the corresponding EM result (and BIC) are contaminated by roundoff error.
G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, 28:781-793 (1995).
A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B, 39:1-22 (1977).
G. J. MacLachlan and K. E. Basford, The EM Algorithm and Extensions, Wiley, (1997).
data(iris) cl <- mhclass(mhtree(iris[,1:4], modelid = "VVV"),3) me( iris[,1:4], modelid = "VVV", ctoz(cl))